It is known to use mass spectrometry to identify microorganisms, and more particularly bacteria. A sample of the microorganism is prepared, after which a mass spectrum of the sample is acquired and pre-processed: spectrum denoising (noise removal), filtering of the background noise (imputable to the detector). The significant peaks of the pre-processed spectrum are then detected and the list of peaks thus obtained is “analyzed” and “compared” with data of a knowledge base built from lists of typical peaks of an identified microorganism or group of microorganisms (strain, genus, family, etc.).
Although this principle seems simple offhand, its implementation is however delicate. Indeed, first, the quantity of information contained in a mass spectrum, and particularly the number of peaks, is very large, which requires very powerful calculation tools to create a robust knowledge base, as well as to implement classification, comparison, and decision algorithms.
There then is a high measurement uncertainty, particularly as concerns the location of speaks in the spectrum. It can indeed be observed that from one measurement to the other on a same spectrometer, as well as from one spectrometer to the other, a peak representing a given molecule does not have a fixed position in the measured spectrums, or at the very least the peak is not contained in a range. Thus, a peak of an acquired spectrum and corresponding to a given protein molecule cannot be identified as corresponding to said protein molecule by the classification algorithm. Finally, this uncertainty is not constant over the range of mass-to-charge ratios and increases as this ratio increases.